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[Project] support SAM inferencer (#2897)

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# Introducing the Segment Anything Model (SAM) Inference Demo!
Welcome to the Segment Anything (SA) Inference Demo, a user-friendly implementation based on the original Segment Anything project. Our demo allows you to experience the power and versatility of the Segment Anything Model (SAM) through an easy-to-use API.
With this inference demo, you can explore the capabilities of the Segment Anything Model and witness its effectiveness in various tasks and image distributions. For more information on the original project, dataset, and model, please visit the official website at https://segment-anything.com.
### Prerequisites
- Python 3.10
- PyTorch 1.13
- MMEngine >= v0.7.2
- MMCV >= v2.0.0
### Installation
We assume that you have already installed PyTorch. If not, please follow the instructions on the [PyTorch website](https://pytorch.org/).
**1. Install MMEngine & MMCV**
```shell
pip install openmim
mim install mmengine
mim install 'mmcv>=2.0.0'
```
**2. Install MMPretrain**
```shell
pip install git+https://github.com/open-mmlab/mmpretrain.git@dev
```
**3. Install MMSegmentation**
```shell
pip install mmsegmentation
```
### Usage
Open the `sam_image_demo.ipynb` notebook and follow the instructions to run the demo.

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from .modeling import * # noqa
from .utils import * # noqa

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .mask_decoder import MaskDecoder
from .prompt_encoder import PromptEncoder
from .sam import SAM
from .transformer import TwoWayTransformer
__all__ = ['SAM', 'MaskDecoder', 'PromptEncoder', 'TwoWayTransformer']

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Type
import torch
import torch.nn as nn
class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = act()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Borrowed from https://github.com/facebookresearch/segment-anything
from typing import List, Tuple
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from mmseg.registry import MODELS
from .common import LayerNorm2d
@MODELS.register_module()
class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: dict,
num_multimask_outputs: int = 3,
act_cfg: dict = dict(type='GELU'),
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
"""Predicts masks given an image and prompt embeddings, using a
tranformer architecture.
Borrowed from https://github.com/facebookresearch/segment-anything
Arguments:
transformer_dim (int): the channel dimension of the transformer
transformer (nn.Module): the transformer used to predict masks
num_multimask_outputs (int): the number of masks to predict
when disambiguating masks
activation (nn.Module): the type of activation to use when
upscaling masks
iou_head_depth (int): the depth of the MLP used to predict
mask quality
iou_head_hidden_dim (int): the hidden dimension of the MLP
used to predict mask quality
"""
super().__init__()
self.transformer_dim = transformer_dim
self.transformer = MODELS.build(transformer)
self.num_multimask_outputs = num_multimask_outputs
self.iou_token = nn.Embedding(1, transformer_dim)
self.num_mask_tokens = num_multimask_outputs + 1
self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
activation = MODELS.build(act_cfg)
self.output_upscaling = nn.Sequential(
nn.ConvTranspose2d(
transformer_dim, transformer_dim // 4, kernel_size=2,
stride=2),
LayerNorm2d(transformer_dim // 4),
activation,
nn.ConvTranspose2d(
transformer_dim // 4,
transformer_dim // 8,
kernel_size=2,
stride=2),
activation,
)
self.output_hypernetworks_mlps = nn.ModuleList([
MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
for i in range(self.num_mask_tokens)
])
self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim,
self.num_mask_tokens, iou_head_depth)
def forward(
self,
image_embeddings: Tensor,
image_pe: Tensor,
sparse_prompt_embeddings: Tensor,
dense_prompt_embeddings: Tensor,
multimask_output: bool,
) -> Tuple[Tensor, Tensor]:
"""Predict masks given image and prompt embeddings.
Borrowed from https://github.com/facebookresearch/segment-anything
Arguments:
image_embeddings (Tensor): the embeddings from the image encoder
image_pe (Tensor): positional encoding with the shape of
image_embeddings
sparse_prompt_embeddings (Tensor): the embeddings of
the points and boxes
dense_prompt_embeddings (Tensor): the embeddings of the mask inputs
multimask_output (bool): Whether to return multiple masks or a single
mask.
Returns:
Tensor: batched predicted masks
Tensor: batched predictions of mask quality
"""
masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
)
# Select the correct mask or masks for output
if multimask_output:
mask_slice = slice(1, None)
else:
mask_slice = slice(0, 1)
masks = masks[:, mask_slice, :, :]
iou_pred = iou_pred[:, mask_slice]
# Prepare output
return masks, iou_pred
def predict_masks(
self,
image_embeddings: Tensor,
image_pe: Tensor,
sparse_prompt_embeddings: Tensor,
dense_prompt_embeddings: Tensor,
) -> Tuple[Tensor, Tensor]:
"""Predicts masks.
See 'forward' for more details.
"""
# Concatenate output tokens
output_tokens = torch.cat(
[self.iou_token.weight, self.mask_tokens.weight], dim=0)
output_tokens = output_tokens.unsqueeze(0).expand(
sparse_prompt_embeddings.size(0), -1, -1)
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
# Expand per-image data in batch direction to be per-mask
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
src = src + dense_prompt_embeddings
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
b, c, h, w = src.shape
# Run the transformer
hs, src = self.transformer(src, pos_src, tokens)
iou_token_out = hs[:, 0, :]
mask_tokens_out = hs[:, 1:(1 + self.num_mask_tokens), :]
# Upscale mask embeddings and predict masks using the mask tokens
src = src.transpose(1, 2).view(b, c, h, w)
upscaled_embedding = self.output_upscaling(src)
hyper_in_list: List[Tensor] = []
for i in range(self.num_mask_tokens):
hyper_in_list.append(self.output_hypernetworks_mlps[i](
mask_tokens_out[:, i, :]))
hyper_in = torch.stack(hyper_in_list, dim=1)
b, c, h, w = upscaled_embedding.shape
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(
b, -1, h, w)
# Generate mask quality predictions
iou_pred = self.iou_prediction_head(iou_token_out)
return masks, iou_pred
# Lightly adapted from
# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
self.sigmoid_output = sigmoid_output
def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Borrowed from https://github.com/facebookresearch/segment-anything
from typing import Any, Optional, Tuple, Type
import numpy as np
import torch
from torch import nn
from mmseg.registry import MODELS
from .common import LayerNorm2d
@MODELS.register_module()
class PromptEncoder(nn.Module):
def __init__(
self,
embed_dim: int,
image_embedding_size: Tuple[int, int],
input_image_size: Tuple[int, int],
mask_in_chans: int,
activation: Type[nn.Module] = nn.GELU,
) -> None:
"""Encodes prompts for input to SAM's mask decoder.
Arguments:
embed_dim (int): The prompts' embedding dimension
image_embedding_size (tuple(int, int)): The spatial size of the
image embedding, as (H, W).
input_image_size (int): The padded size of the image as input
to the image encoder, as (H, W).
mask_in_chans (int): The number of hidden channels used for
encoding input masks.
activation (nn.Module): The activation to use when encoding
input masks.
"""
super().__init__()
self.embed_dim = embed_dim
self.input_image_size = input_image_size
self.image_embedding_size = image_embedding_size
self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
point_embeddings = [
nn.Embedding(1, embed_dim)
for i in range(self.num_point_embeddings)
]
self.point_embeddings = nn.ModuleList(point_embeddings)
self.not_a_point_embed = nn.Embedding(1, embed_dim)
self.mask_input_size = (4 * image_embedding_size[0],
4 * image_embedding_size[1])
self.mask_downscaling = nn.Sequential(
nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
LayerNorm2d(mask_in_chans // 4),
activation(),
nn.Conv2d(
mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
LayerNorm2d(mask_in_chans),
activation(),
nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
)
self.no_mask_embed = nn.Embedding(1, embed_dim)
def get_dense_pe(self) -> torch.Tensor:
"""Returns the positional encoding used to encode point prompts,
applied to a dense set of points the shape of the image encoding.
Returns:
torch.Tensor: Positional encoding with shape
1x(embed_dim)x(embedding_h)x(embedding_w)
"""
return self.pe_layer(self.image_embedding_size).unsqueeze(0)
def _embed_points(
self,
points: torch.Tensor,
labels: torch.Tensor,
pad: bool,
) -> torch.Tensor:
"""Embeds point prompts."""
points = points + 0.5 # Shift to center of pixel
if pad:
padding_point = torch.zeros((points.shape[0], 1, 2),
device=points.device)
padding_label = -torch.ones(
(labels.shape[0], 1), device=labels.device)
points = torch.cat([points, padding_point], dim=1)
labels = torch.cat([labels, padding_label], dim=1)
point_embedding = self.pe_layer.forward_with_coords(
points, self.input_image_size)
point_embedding[labels == -1] = 0.0
point_embedding[labels == -1] += self.not_a_point_embed.weight
point_embedding[labels == 0] += self.point_embeddings[0].weight
point_embedding[labels == 1] += self.point_embeddings[1].weight
return point_embedding
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
"""Embeds box prompts."""
boxes = boxes + 0.5 # Shift to center of pixel
coords = boxes.reshape(-1, 2, 2)
corner_embedding = self.pe_layer.forward_with_coords(
coords, self.input_image_size)
corner_embedding[:, 0, :] += self.point_embeddings[2].weight
corner_embedding[:, 1, :] += self.point_embeddings[3].weight
return corner_embedding
def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
"""Embeds mask inputs."""
mask_embedding = self.mask_downscaling(masks)
return mask_embedding
def _get_batch_size(
self,
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
boxes: Optional[torch.Tensor],
masks: Optional[torch.Tensor],
) -> int:
"""Gets the batch size of the output given the batch size of the input
prompts."""
if points is not None:
return points[0].shape[0]
elif boxes is not None:
return boxes.shape[0]
elif masks is not None:
return masks.shape[0]
else:
return 1
def _get_device(self) -> torch.device:
return self.point_embeddings[0].weight.device
def forward(
self,
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
boxes: Optional[torch.Tensor],
masks: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Embeds different types of prompts, returning both sparse and dense
embeddings.
Arguments:
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
and labels to embed.
boxes (torch.Tensor or none): boxes to embed
masks (torch.Tensor or none): masks to embed
Returns:
torch.Tensor: sparse embeddings for the points and boxes, with shape
BxNx(embed_dim), where N is determined by the number of input points
and boxes.
torch.Tensor: dense embeddings for the masks, in the shape
Bx(embed_dim)x(embed_H)x(embed_W)
""" # noqa
bs = self._get_batch_size(points, boxes, masks)
sparse_embeddings = torch.empty((bs, 0, self.embed_dim),
device=self._get_device())
if points is not None:
coords, labels = points
point_embeddings = self._embed_points(
coords, labels, pad=(boxes is None))
sparse_embeddings = torch.cat(
[sparse_embeddings, point_embeddings], dim=1)
if boxes is not None:
box_embeddings = self._embed_boxes(boxes)
sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings],
dim=1)
if masks is not None:
dense_embeddings = self._embed_masks(masks)
else:
dense_embeddings = self.no_mask_embed.weight.reshape(
1, -1, 1, 1).expand(bs, -1, self.image_embedding_size[0],
self.image_embedding_size[1])
return sparse_embeddings, dense_embeddings
class PositionEmbeddingRandom(nn.Module):
"""Positional encoding using random spatial frequencies."""
def __init__(self,
num_pos_feats: int = 64,
scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_buffer(
'positional_encoding_gaussian_matrix',
scale * torch.randn((2, num_pos_feats)),
)
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
"""Positionally encode points that are normalized to [0,1]."""
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape # noqa
coords = 2 * coords - 1
coords = coords @ self.positional_encoding_gaussian_matrix
coords = 2 * np.pi * coords
# outputs d_1 x ... x d_n x C shape
return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
def forward(self, size: Tuple[int, int]) -> torch.Tensor:
"""Generate positional encoding for a grid of the specified size."""
h, w = size
device: Any = self.positional_encoding_gaussian_matrix.device
grid = torch.ones((h, w), device=device, dtype=torch.float32)
y_embed = grid.cumsum(dim=0) - 0.5
x_embed = grid.cumsum(dim=1) - 0.5
y_embed = y_embed / h
x_embed = x_embed / w
pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
return pe.permute(2, 0, 1) # C x H x W
def forward_with_coords(self, coords_input: torch.Tensor,
image_size: Tuple[int, int]) -> torch.Tensor:
"""Positionally encode points that are not normalized to [0,1]."""
coords = coords_input.clone()
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
return self._pe_encoding(coords.to(torch.float)) # B x N x C

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Borrowed from https://github.com/facebookresearch/segment-anything
from typing import Any, Dict, List, Tuple
import torch
from torch import nn
from torch.nn import functional as F
from mmseg.registry import MODELS
from .mask_decoder import MaskDecoder
from .prompt_encoder import PromptEncoder
@MODELS.register_module()
class SAM(nn.Module):
mask_threshold: float = 0.0
image_format: str = 'RGB'
def __init__(
self,
image_encoder_cfg: dict,
prompt_encoder_cfg: dict,
mask_decoder_cfg: dict,
pixel_mean: List[float] = [123.675, 116.28, 103.53],
pixel_std: List[float] = [58.395, 57.12, 57.375],
) -> None:
"""SAM predicts object masks from an image and input prompts. Borrowed
from https://github.com/facebookresearch/segment-anything.
Arguments:
image_encoder (ViTSAM): The backbone used to encode the
image into image embeddings that allow for efficient mask
prediction.
prompt_encoder (PromptEncoder): Encodes various types of input
prompts.
mask_decoder (MaskDecoder): Predicts masks from the image embeddings
and encoded prompts.
pixel_mean (list(float)): Mean values for normalizing pixels in the
input image.
pixel_std (list(float)): Std values for normalizing pixels in the
input image.
"""
super().__init__()
self.image_encoder = MODELS.build(image_encoder_cfg)
self.prompt_encoder: PromptEncoder = MODELS.build(prompt_encoder_cfg)
self.mask_decoder: MaskDecoder = MODELS.build(mask_decoder_cfg)
self.register_buffer('pixel_mean',
torch.Tensor(pixel_mean).view(-1, 1, 1), False)
self.register_buffer('pixel_std',
torch.Tensor(pixel_std).view(-1, 1, 1), False)
@property
def device(self) -> Any:
return self.pixel_mean.device
@torch.no_grad()
def forward(
self,
batched_input: List[Dict[str, Any]],
multimask_output: bool,
) -> List[Dict[str, torch.Tensor]]:
"""Predicts masks end-to-end from provided images and prompts. If
prompts are not known in advance, using SamPredictor is recommended
over calling the model directly.
Borrowed from https://github.com/facebookresearch/segment-anything
Arguments:
batched_input (list(dict)): A list over input images, each a
dictionary with the following keys. A prompt key can be
excluded if it is not present.
'image': The image as a torch tensor in 3xHxW format,
already transformed for input to the model.
'original_size': (tuple(int, int)) The original size of
the image before transformation, as (H, W).
'point_coords': (torch.Tensor) Batched point prompts for
this image, with shape BxNx2. Already transformed to the
input frame of the model.
'point_labels': (torch.Tensor) Batched labels for point prompts,
with shape BxN.
'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
Already transformed to the input frame of the model.
'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
in the form Bx1xHxW.
multimask_output (bool): Whether the model should predict multiple
disambiguating masks, or return a single mask.
Returns:
(list(dict)): A list over input images, where each element is
as dictionary with the following keys.
'masks': (torch.Tensor) Batched binary mask predictions,
with shape BxCxHxW, where B is the number of input prompts,
C is determiend by multimask_output, and (H, W) is the
original size of the image.
'iou_predictions': (torch.Tensor) The model's predictions
of mask quality, in shape BxC.
'low_res_logits': (torch.Tensor) Low resolution logits with
shape BxCxHxW, where H=W=256. Can be passed as mask input
to subsequent iterations of prediction.
"""
input_images = torch.stack(
[self.preprocess(x['image']) for x in batched_input], dim=0)
image_embeddings = self.image_encoder(input_images)
outputs = []
for image_record, curr_embedding in zip(batched_input,
image_embeddings):
if 'point_coords' in image_record:
points = (image_record['point_coords'],
image_record['point_labels'])
else:
points = None
sparse_embeddings, dense_embeddings = self.prompt_encoder(
points=points,
boxes=image_record.get('boxes', None),
masks=image_record.get('mask_inputs', None),
)
low_res_masks, iou_predictions = self.mask_decoder(
image_embeddings=curr_embedding.unsqueeze(0),
image_pe=self.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output,
)
masks = self.postprocess_masks(
low_res_masks,
input_size=image_record['image'].shape[-2:],
original_size=image_record['original_size'],
)
masks = masks > self.mask_threshold
outputs.append({
'masks': masks,
'iou_predictions': iou_predictions,
'low_res_logits': low_res_masks,
})
return outputs
def postprocess_masks(
self,
masks: torch.Tensor,
input_size: Tuple[int, ...],
original_size: Tuple[int, ...],
) -> torch.Tensor:
"""Remove padding and upscale masks to the original image size.
Borrowed from https://github.com/facebookresearch/segment-anything
Arguments:
masks (torch.Tensor): Batched masks from the mask_decoder,
in BxCxHxW format.
input_size (tuple(int, int)): The size of the image input to the
model, in (H, W) format. Used to remove padding.
original_size (tuple(int, int)): The original size of the image
before resizing for input to the model, in (H, W) format.
Returns:
(torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
is given by original_size.
"""
masks = F.interpolate(
masks,
self.image_encoder.img_size,
mode='bilinear',
align_corners=False,
)
masks = masks[..., :input_size[0], :input_size[1]]
masks = F.interpolate(
masks, original_size, mode='bilinear', align_corners=False)
return masks
def preprocess(self, x: torch.Tensor) -> torch.Tensor:
"""Normalize pixel values and pad to a square input."""
# Normalize colors
x = (x - self.pixel_mean) / self.pixel_std
# Pad
h, w = x.shape[-2:]
img_size = max(self.image_encoder.img_size)
padh = img_size - h
padw = img_size - w
x = F.pad(x, (0, padw, 0, padh))
return x

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Tuple, Type
import torch
from torch import Tensor, nn
from mmseg.registry import MODELS
from .common import MLPBlock
@MODELS.register_module()
class TwoWayTransformer(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
"""A transformer decoder that attends to an input image using queries
whose positional embedding is supplied.
Args:
depth (int): number of layers in the transformer
embedding_dim (int): the channel dimension for the input embeddings
num_heads (int): the number of heads for multihead attention. Must
divide embedding_dim
mlp_dim (int): the channel dimension internal to the MLP block
activation (nn.Module): the activation to use in the MLP block
"""
super().__init__()
self.depth = depth
self.embedding_dim = embedding_dim
self.num_heads = num_heads
self.mlp_dim = mlp_dim
self.layers = nn.ModuleList()
for i in range(depth):
self.layers.append(
TwoWayAttentionBlock(
embedding_dim=embedding_dim,
num_heads=num_heads,
mlp_dim=mlp_dim,
activation=activation,
attention_downsample_rate=attention_downsample_rate,
skip_first_layer_pe=(i == 0),
))
self.final_attn_token_to_image = Attention(
embedding_dim,
num_heads,
downsample_rate=attention_downsample_rate)
self.norm_final_attn = nn.LayerNorm(embedding_dim)
def forward(
self,
image_embedding: Tensor,
image_pe: Tensor,
point_embedding: Tensor,
) -> Tuple[Tensor, Tensor]:
"""
Args:
image_embedding (torch.Tensor): image to attend to. Should be shape
B x embedding_dim x h x w for any h and w.
image_pe (torch.Tensor): the positional encoding to add to the image. Must
have the same shape as image_embedding.
point_embedding (torch.Tensor): the embedding to add to the query points.
Must have shape B x N_points x embedding_dim for any N_points.
Returns:
torch.Tensor: the processed point_embedding
torch.Tensor: the processed image_embedding
""" # noqa E501
# BxCxHxW -> BxHWxC == B x N_image_tokens x C
bs, c, h, w = image_embedding.shape
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
# Prepare queries
queries = point_embedding
keys = image_embedding
# Apply transformer blocks and final layernorm
for layer in self.layers:
queries, keys = layer(
queries=queries,
keys=keys,
query_pe=point_embedding,
key_pe=image_pe,
)
# Apply the final attenion layer from the points to the image
q = queries + point_embedding
k = keys + image_pe
attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm_final_attn(queries)
return queries, keys
class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""A transformer block with four layers: (1) self-attention of sparse
inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
block on sparse inputs, and (4) cross attention of dense inputs to
sparse inputs.
Arguments:
embedding_dim (int): the channel dimension of the embeddings
num_heads (int): the number of heads in the attention layers
mlp_dim (int): the hidden dimension of the mlp block
activation (nn.Module): the activation of the mlp block
skip_first_layer_pe (bool): skip the PE on the first layer
"""
super().__init__()
self.self_attn = Attention(embedding_dim, num_heads)
self.norm1 = nn.LayerNorm(embedding_dim)
self.cross_attn_token_to_image = Attention(
embedding_dim,
num_heads,
downsample_rate=attention_downsample_rate)
self.norm2 = nn.LayerNorm(embedding_dim)
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
self.norm3 = nn.LayerNorm(embedding_dim)
self.norm4 = nn.LayerNorm(embedding_dim)
self.cross_attn_image_to_token = Attention(
embedding_dim,
num_heads,
downsample_rate=attention_downsample_rate)
self.skip_first_layer_pe = skip_first_layer_pe
def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor,
key_pe: Tensor) -> Tuple[Tensor, Tensor]:
# Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(queries)
# Cross attention block, tokens attending to image embedding
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm2(queries)
# MLP block
mlp_out = self.mlp(queries)
queries = queries + mlp_out
queries = self.norm3(queries)
# Cross attention block, image embedding attending to tokens
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
keys = keys + attn_out
keys = self.norm4(keys)
return queries, keys
class Attention(nn.Module):
"""An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values."""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super().__init__()
self.embedding_dim = embedding_dim
self.internal_dim = embedding_dim // downsample_rate
self.num_heads = num_heads
assert self.internal_dim % num_heads == 0, 'num_heads must divide embedding_dim.' # noqa E501
self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
def _recombine_heads(self, x: Tensor) -> Tensor:
b, n_heads, n_tokens, c_per_head = x.shape
x = x.transpose(1, 2)
return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
# Input projections
q = self.q_proj(q)
k = self.k_proj(k)
v = self.v_proj(v)
# Separate into heads
q = self._separate_heads(q, self.num_heads)
k = self._separate_heads(k, self.num_heads)
v = self._separate_heads(v, self.num_heads)
# Attention
_, _, _, c_per_head = q.shape
attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
attn = attn / math.sqrt(c_per_head)
attn = torch.softmax(attn, dim=-1)
# Get output
out = attn @ v
out = self._recombine_heads(out)
out = self.out_proj(out)
return out

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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import torch
from mmengine.runner.checkpoint import load_checkpoint
# yapf: disable
from sam.utils import (MaskData, area_from_rle, batch_iterator,
batched_mask_to_box, box_xyxy_to_xywh,
build_all_layer_point_grids, calculate_stability_score,
coco_encode_rle, generate_crop_boxes,
is_box_near_crop_edge, mask_to_rle_pytorch,
remove_small_regions, rle_to_mask, uncrop_boxes_xyxy,
uncrop_masks, uncrop_points)
from torchvision.ops.boxes import batched_nms, box_area
from mmseg.registry import MODELS, TRANSFORMS
# yapf: enable
model_zoo = {
'base':
'https://download.openmmlab.com/mmsegmentation/v0.5/sam/sam_vit-base-p16_3rdparty_sa1b-1024x1024_20230413-78a25eed.pth', # noqa
'large':
'https://download.openmmlab.com/mmsegmentation/v0.5/sam/sam_vit-large-p16_3rdparty_sa1b-1024x1024_20230413-940520da.pth', # noqa
'huge':
'https://download.openmmlab.com/mmsegmentation/v0.5/sam/sam_vit-huge-p16_3rdparty_sa1b-1024x1024_20230413-faaf96f6.pth', # noqa
}
class SAMInferencer:
def __init__(self, arch: str = 'base') -> None:
assert arch in ['base', 'large', 'huge']
self.model = self.init_model(arch)
self.transform = TRANSFORMS.build(
dict(
type='ResizeLongestSide',
target_length=max(self.model.image_encoder.img_size)))
def set_image(
self,
image: np.ndarray,
image_format: str = 'RGB',
) -> None:
"""Calculates the image embeddings for the provided image, allowing
masks to be predicted with the 'predict' method.
Arguments:
image (np.ndarray): The image for calculating masks. Expects an
image in HWC uint8 format, with pixel values in [0, 255].
image_format (str): The color format of the image, in ['RGB', 'BGR'].
"""
assert image_format in [
'RGB',
'BGR',
], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
if image_format != self.model.image_format:
image = image[..., ::-1]
# Transform the image to the form expected by the model
input_image = self.transform.apply_image(image)
input_image_torch = torch.as_tensor(input_image, device=self.device)
input_image_torch = input_image_torch.permute(
2, 0, 1).contiguous()[None, :, :, :]
self.set_torch_image(input_image_torch, image.shape[:2])
@torch.no_grad()
def set_torch_image(
self,
transformed_image: torch.Tensor,
original_image_size: Tuple[int, ...],
) -> None:
"""Calculates the image embeddings for the provided image, allowing
masks to be predicted with the 'predict' method. Expects the input
image to be already transformed to the format expected by the model.
Arguments:
transformed_image (torch.Tensor): The input image, with shape
1x3xHxW, which has been transformed with ResizeLongestSide.
original_image_size (tuple(int, int)): The size of the image
before transformation, in (H, W) format.
"""
assert (len(transformed_image.shape) == 4
and transformed_image.shape[1] == 3
and max(*transformed_image.shape[2:]) == max(
self.model.image_encoder.img_size)
), 'set_torch_image input must be BCHW with long side'
f' {self.model.image_encoder.img_size}.'
self.reset_image()
self.original_size = original_image_size
self.input_size = tuple(transformed_image.shape[-2:])
input_image = self.model.preprocess(transformed_image)
self.features = self.model.image_encoder(input_image)[0]
self.is_image_set = True
def predict(
self,
point_coords: Optional[np.ndarray] = None,
point_labels: Optional[np.ndarray] = None,
box: Optional[np.ndarray] = None,
mask_input: Optional[np.ndarray] = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Predict masks for the given input prompts, using the currently set
image.
Arguments:
point_coords (np.ndarray or None): A Nx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (np.ndarray or None): A length N array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A length 4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form 1xHxW, where
for SAM, H=W=256.
multimask_output (bool): If true, the model will return three masks.
For ambiguous input prompts (such as a single click), this will often
produce better masks than a single prediction. If only a single
mask is needed, the model's predicted quality score can be used
to select the best mask. For non-ambiguous prompts, such as multiple
input prompts, multimask_output=False can give better results.
return_logits (bool): If true, returns un-thresholded masks logits
instead of a binary mask.
Returns:
(np.ndarray): The output masks in CxHxW format, where C is the
number of masks, and (H, W) is the original image size.
(np.ndarray): An array of length C containing the model's
predictions for the quality of each mask.
(np.ndarray): An array of shape CxHxW, where C is the number
of masks and H=W=256. These low resolution logits can be passed to
a subsequent iteration as mask input.
""" # noqa
if not self.is_image_set:
raise RuntimeError(
'An image must be set with .set_image(...) before mask'
'prediction.')
# Transform input prompts
coords_torch = None
labels_torch = None
box_torch = None
mask_input_torch = None
if point_coords is not None:
assert (
point_labels is not None
), 'point_labels must be supplied if point_coords is supplied.'
point_coords = self.transform.apply_coords(point_coords,
self.original_size)
coords_torch = torch.as_tensor(
point_coords, dtype=torch.float, device=self.device)
labels_torch = torch.as_tensor(
point_labels, dtype=torch.int, device=self.device)
coords_torch, labels_torch = coords_torch[
None, :, :], labels_torch[None, :]
if box is not None:
box = self.transform.apply_boxes(box, self.original_size)
box_torch = torch.as_tensor(
box, dtype=torch.float, device=self.device)
box_torch = box_torch[None, :]
if mask_input is not None:
mask_input_torch = torch.as_tensor(
mask_input, dtype=torch.float, device=self.device)
mask_input_torch = mask_input_torch[None, :, :, :]
masks, iou_predictions, low_res_masks = self.predict_torch(
coords_torch,
labels_torch,
box_torch,
mask_input_torch,
multimask_output,
return_logits=return_logits,
)
masks = masks[0].detach().cpu().numpy()
iou_predictions = iou_predictions[0].detach().cpu().numpy()
low_res_masks = low_res_masks[0].detach().cpu().numpy()
return masks, iou_predictions, low_res_masks
@torch.no_grad()
def predict_torch(
self,
point_coords: Optional[torch.Tensor],
point_labels: Optional[torch.Tensor],
boxes: Optional[torch.Tensor] = None,
mask_input: Optional[torch.Tensor] = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Predict masks for the given input prompts, using the currently set
image. Input prompts are batched torch tensors and are expected to
already be transformed to the input frame using ResizeLongestSide.
Arguments:
point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (torch.Tensor or None): A BxN array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A Bx4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form Bx1xHxW, where
for SAM, H=W=256. Masks returned by a previous iteration of the
predict method do not need further transformation.
multimask_output (bool): If true, the model will return three masks.
For ambiguous input prompts (such as a single click), this will often
produce better masks than a single prediction. If only a single
mask is needed, the model's predicted quality score can be used
to select the best mask. For non-ambiguous prompts, such as multiple
input prompts, multimask_output=False can give better results.
return_logits (bool): If true, returns un-thresholded masks logits
instead of a binary mask.
Returns:
(torch.Tensor): The output masks in BxCxHxW format, where C is the
number of masks, and (H, W) is the original image size.
(torch.Tensor): An array of shape BxC containing the model's
predictions for the quality of each mask.
(torch.Tensor): An array of shape BxCxHxW, where C is the number
of masks and H=W=256. These low res logits can be passed to
a subsequent iteration as mask input.
""" # noqa
if not self.is_image_set:
raise RuntimeError(
'An image must be set with .set_image(...) before mask '
'prediction.')
if point_coords is not None:
points = (point_coords, point_labels)
else:
points = None
# Embed prompts
sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
points=points,
boxes=boxes,
masks=mask_input,
)
# Predict masks
low_res_masks, iou_predictions = self.model.mask_decoder(
image_embeddings=self.features,
image_pe=self.model.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output,
)
# Upscale the masks to the original image resolution
masks = self.model.postprocess_masks(low_res_masks, self.input_size,
self.original_size)
if not return_logits:
masks = masks > self.model.mask_threshold
return masks, iou_predictions, low_res_masks
def get_image_embedding(self) -> torch.Tensor:
"""Returns the image embeddings for the currently set image, with shape
1xCxHxW, where C is the embedding dimension and (H,W) are the embedding
spatial dimension of SAM (typically C=256, H=W=64)."""
if not self.is_image_set:
raise RuntimeError(
'An image must be set with .set_image(...) to generate an '
'embedding.')
assert self.features is not None, 'Features must exist if an image has'
' been set.'
return self.features
@property
def device(self) -> torch.device:
return self.model.device
def reset_image(self) -> None:
"""Resets the currently set image."""
self.is_image_set = False
self.features = None
self.orig_h = None
self.orig_w = None
self.input_h = None
self.input_w = None
def init_model(self, arch: str):
model = MODELS.build(
dict(
type='SAM',
image_encoder_cfg=dict(
type='mmpretrain.ViTSAM',
arch=arch,
img_size=1024,
patch_size=16,
out_channels=256,
use_abs_pos=True,
use_rel_pos=True,
window_size=14,
),
prompt_encoder_cfg=dict(
type='PromptEncoder',
embed_dim=256,
image_embedding_size=(64, 64),
input_image_size=(1024, 1024),
mask_in_chans=16,
),
mask_decoder_cfg=dict(
type='MaskDecoder',
num_multimask_outputs=3,
transformer=dict(
type='TwoWayTransformer',
depth=2,
embedding_dim=256,
mlp_dim=2048,
num_heads=8,
),
transformer_dim=256,
iou_head_depth=3,
iou_head_hidden_dim=256,
)))
load_checkpoint(model, model_zoo.get(arch), strict=True)
if torch.cuda.is_available():
model = model.cuda()
return model
class SamAutomaticMaskGenerator:
def __init__(
self,
arch: str = 'base',
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = 'binary_mask',
) -> None:
"""Using a SAM model, generates masks for the entire image. Generates a
grid of point prompts over the image, then filters low quality and
duplicate masks. The default settings are chosen for SAM with a ViT-H
backbone.
Arguments:
arch (str): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crops_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crops_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
""" # noqa
assert (points_per_side is None) != (
point_grids is None
), 'Exactly one of points_per_side or point_grid must be provided.'
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError(
"Can't have both points_per_side and point_grid be None.")
assert output_mode in [
'binary_mask',
'uncompressed_rle',
'coco_rle',
], f'Unknown output_mode {output_mode}.'
if output_mode == 'coco_rle':
from pycocotools import \
mask as mask_utils # type: ignore # noqa: F401
if min_mask_region_area > 0:
import cv2 # type: ignore # noqa: F401
self.predictor = SAMInferencer(arch)
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
""" # noqa
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == 'coco_rle':
mask_data['segmentations'] = [
coco_encode_rle(rle) for rle in mask_data['rles']
]
elif self.output_mode == 'binary_mask':
mask_data['segmentations'] = [
rle_to_mask(rle) for rle in mask_data['rles']
]
else:
mask_data['segmentations'] = mask_data['rles']
# Write mask records
curr_anns = []
for idx in range(len(mask_data['segmentations'])):
ann = {
'segmentation':
mask_data['segmentations'][idx],
'area':
area_from_rle(mask_data['rles'][idx]),
'bbox':
box_xyxy_to_xywh(mask_data['boxes'][idx]).tolist(),
'predicted_iou':
mask_data['iou_preds'][idx].item(),
'point_coords': [mask_data['points'][idx].tolist()],
'stability_score':
mask_data['stability_score'][idx].item(),
'crop_box':
box_xyxy_to_xywh(mask_data['crop_boxes'][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[:2]
crop_boxes, layer_idxs = generate_crop_boxes(orig_size,
self.crop_n_layers,
self.crop_overlap_ratio)
# Iterate over image crops
data = MaskData()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx,
orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data['crop_boxes'])
scores = scores.to(data['boxes'].device)
keep_by_nms = batched_nms(
data['boxes'].float(),
scores,
torch.zeros(len(data['boxes'])), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[:2]
self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points, ) in batch_iterator(self.points_per_batch,
points_for_image):
batch_data = self._process_batch(points, cropped_im_size, crop_box,
orig_size)
data.cat(batch_data)
del batch_data
self.predictor.reset_image()
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data['boxes'].float(),
data['iou_preds'],
torch.zeros(len(data['boxes'])), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data['boxes'] = uncrop_boxes_xyxy(data['boxes'], crop_box)
data['points'] = uncrop_points(data['points'], crop_box)
data['crop_boxes'] = torch.tensor(
[crop_box for _ in range(len(data['rles']))])
return data
def _process_batch(
self,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(
points, im_size)
in_points = torch.as_tensor(
transformed_points, device=self.predictor.device)
in_labels = torch.ones(
in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
multimask_output=True,
return_logits=True,
)
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data['iou_preds'] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data['stability_score'] = calculate_stability_score(
data['masks'], self.predictor.model.mask_threshold,
self.stability_score_offset)
if self.stability_score_thresh > 0.0:
keep_mask = data['stability_score'] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data['masks'] = data['masks'] > self.predictor.model.mask_threshold
data['boxes'] = batched_mask_to_box(data['masks'])
# Filter boxes that touch crop boundaries
keep_mask = ~is_box_near_crop_edge(data['boxes'], crop_box,
[0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
data.filter(keep_mask)
# Compress to RLE
data['masks'] = uncrop_masks(data['masks'], crop_box, orig_h, orig_w)
data['rles'] = mask_to_rle_pytorch(data['masks'])
del data['masks']
return data
@staticmethod
def postprocess_small_regions(mask_data: MaskData, min_area: int,
nms_thresh: float) -> MaskData:
"""Removes small disconnected regions and holes in masks, then reruns
box NMS to remove any new duplicates.
Edits mask_data in place.
Requires open-cv as a dependency.
"""
if len(mask_data['rles']) == 0:
return mask_data
# Filter small disconnected regions and holes
new_masks = []
scores = []
for rle in mask_data['rles']:
mask = rle_to_mask(rle)
mask, changed = remove_small_regions(mask, min_area, mode='holes')
unchanged = not changed
mask, changed = remove_small_regions(
mask, min_area, mode='islands')
unchanged = unchanged and not changed
new_masks.append(torch.as_tensor(mask).unsqueeze(0))
# Give score=0 to changed masks and score=1 to unchanged masks
# so NMS will prefer ones that didn't need postprocessing
scores.append(float(unchanged))
# Recalculate boxes and remove any new duplicates
masks = torch.cat(new_masks, dim=0)
boxes = batched_mask_to_box(masks)
keep_by_nms = batched_nms(
boxes.float(),
torch.as_tensor(scores),
torch.zeros(len(boxes)), # categories
iou_threshold=nms_thresh,
)
# Only recalculate RLEs for masks that have changed
for i_mask in keep_by_nms:
if scores[i_mask] == 0.0:
mask_torch = masks[i_mask].unsqueeze(0)
mask_data['rles'][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
mask_data['boxes'][i_mask] = boxes[
i_mask] # update res directly
mask_data.filter(keep_by_nms)
return mask_data

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from .amg import * # noqa: F403 F401
from .transforms import ResizeLongestSide # noqa: F403 F401

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# https://github.com/facebookresearch/segment-anything
import math
from copy import deepcopy
from itertools import product
from typing import Any, Dict, Generator, ItemsView, List, Tuple
import numpy as np
import torch
class MaskData:
"""A structure for storing masks and their related data in batched format.
Implements basic filtering and concatenation.
"""
def __init__(self, **kwargs) -> None:
for v in kwargs.values():
assert isinstance(
v, (list, np.ndarray, torch.Tensor)
), 'MaskData only supports list, numpy arrays, and torch tensors.'
self._stats = dict(**kwargs)
def __setitem__(self, key: str, item: Any) -> None:
assert isinstance(
item, (list, np.ndarray, torch.Tensor)
), 'MaskData only supports list, numpy arrays, and torch tensors.'
self._stats[key] = item
def __delitem__(self, key: str) -> None:
del self._stats[key]
def __getitem__(self, key: str) -> Any:
return self._stats[key]
def items(self) -> ItemsView[str, Any]:
return self._stats.items()
def filter(self, keep: torch.Tensor) -> None:
for k, v in self._stats.items():
if v is None:
self._stats[k] = None
elif isinstance(v, torch.Tensor):
self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
elif isinstance(v, np.ndarray):
self._stats[k] = v[keep.detach().cpu().numpy()]
elif isinstance(v, list) and keep.dtype == torch.bool:
self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
elif isinstance(v, list):
self._stats[k] = [v[i] for i in keep]
else:
raise TypeError(
f'MaskData key {k} has an unsupported type {type(v)}.')
def cat(self, new_stats: 'MaskData') -> None:
for k, v in new_stats.items():
if k not in self._stats or self._stats[k] is None:
self._stats[k] = deepcopy(v)
elif isinstance(v, torch.Tensor):
self._stats[k] = torch.cat([self._stats[k], v], dim=0)
elif isinstance(v, np.ndarray):
self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
elif isinstance(v, list):
self._stats[k] = self._stats[k] + deepcopy(v)
else:
raise TypeError(
f'MaskData key {k} has an unsupported type {type(v)}.')
def to_numpy(self) -> None:
for k, v in self._stats.items():
if isinstance(v, torch.Tensor):
self._stats[k] = v.detach().cpu().numpy()
def is_box_near_crop_edge(boxes: torch.Tensor,
crop_box: List[int],
orig_box: List[int],
atol: float = 20.0) -> torch.Tensor:
"""Filter masks at the edge of a crop, but not at the edge of the original
image."""
crop_box_torch = torch.as_tensor(
crop_box, dtype=torch.float, device=boxes.device)
orig_box_torch = torch.as_tensor(
orig_box, dtype=torch.float, device=boxes.device)
boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
near_crop_edge = torch.isclose(
boxes, crop_box_torch[None, :], atol=atol, rtol=0)
near_image_edge = torch.isclose(
boxes, orig_box_torch[None, :], atol=atol, rtol=0)
near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
return torch.any(near_crop_edge, dim=1)
def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
box_xywh = deepcopy(box_xyxy)
box_xywh[2] = box_xywh[2] - box_xywh[0]
box_xywh[3] = box_xywh[3] - box_xywh[1]
return box_xywh
def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
assert len(args) > 0 and all(
len(a) == len(args[0]) for a in
args), 'Batched iteration must have inputs of all the same size.'
n_batches = len(args[0]) // batch_size + int(
len(args[0]) % batch_size != 0)
for b in range(n_batches):
yield [arg[b * batch_size:(b + 1) * batch_size] for arg in args]
def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
"""Encodes masks to an uncompressed RLE, in the format expected by pycoco
tools."""
# Put in fortran order and flatten h,w
b, h, w = tensor.shape
tensor = tensor.permute(0, 2, 1).flatten(1)
# Compute change indices
diff = tensor[:, 1:] ^ tensor[:, :-1]
change_indices = diff.nonzero()
# Encode run length
out = []
for i in range(b):
cur_idxs = change_indices[change_indices[:, 0] == i, 1]
cur_idxs = torch.cat([
torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
cur_idxs + 1,
torch.tensor([h * w], dtype=cur_idxs.dtype,
device=cur_idxs.device),
])
btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
counts = [] if tensor[i, 0] == 0 else [0]
counts.extend(btw_idxs.detach().cpu().tolist())
out.append({'size': [h, w], 'counts': counts})
return out
def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
"""Compute a binary mask from an uncompressed RLE."""
h, w = rle['size']
mask = np.empty(h * w, dtype=bool)
idx = 0
parity = False
for count in rle['counts']:
mask[idx:idx + count] = parity
idx += count
parity ^= True
mask = mask.reshape(w, h)
return mask.transpose() # Put in C order
def area_from_rle(rle: Dict[str, Any]) -> int:
return sum(rle['counts'][1::2])
def calculate_stability_score(masks: torch.Tensor, mask_threshold: float,
threshold_offset: float) -> torch.Tensor:
"""Computes the stability score for a batch of masks.
The stability score is the IoU between the binary masks obtained by
thresholding the predicted mask logits at high and low values.
"""
# One mask is always contained inside the other.
# Save memory by preventing unnecessary cast to torch.int64
intersections = ((masks > (mask_threshold + threshold_offset)).sum(
-1, dtype=torch.int16).sum(-1, dtype=torch.int32))
unions = ((masks > (mask_threshold - threshold_offset)).sum(
-1, dtype=torch.int16).sum(-1, dtype=torch.int32))
return intersections / unions
def build_point_grid(n_per_side: int) -> np.ndarray:
"""Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
offset = 1 / (2 * n_per_side)
points_one_side = np.linspace(offset, 1 - offset, n_per_side)
points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
points_y = np.tile(points_one_side[:, None], (1, n_per_side))
points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
return points
def build_all_layer_point_grids(n_per_side: int, n_layers: int,
scale_per_layer: int) -> List[np.ndarray]:
"""Generates point grids for all crop layers."""
points_by_layer = []
for i in range(n_layers + 1):
n_points = int(n_per_side / (scale_per_layer**i))
points_by_layer.append(build_point_grid(n_points))
return points_by_layer
def generate_crop_boxes(
im_size: Tuple[int, ...], n_layers: int,
overlap_ratio: float) -> Tuple[List[List[int]], List[int]]:
"""Generates a list of crop boxes of different sizes.
Each layer has (2**i)**2 boxes for the ith layer.
"""
crop_boxes, layer_idxs = [], []
im_h, im_w = im_size
short_side = min(im_h, im_w)
# Original image
crop_boxes.append([0, 0, im_w, im_h])
layer_idxs.append(0)
def crop_len(orig_len, n_crops, overlap):
return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
for i_layer in range(n_layers):
n_crops_per_side = 2**(i_layer + 1)
overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
crop_w = crop_len(im_w, n_crops_per_side, overlap)
crop_h = crop_len(im_h, n_crops_per_side, overlap)
crop_box_x0 = [
int((crop_w - overlap) * i) for i in range(n_crops_per_side)
]
crop_box_y0 = [
int((crop_h - overlap) * i) for i in range(n_crops_per_side)
]
# Crops in XYWH format
for x0, y0 in product(crop_box_x0, crop_box_y0):
box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
crop_boxes.append(box)
layer_idxs.append(i_layer + 1)
return crop_boxes, layer_idxs
def uncrop_boxes_xyxy(boxes: torch.Tensor,
crop_box: List[int]) -> torch.Tensor:
x0, y0, _, _ = crop_box
offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
# Check if boxes has a channel dimension
if len(boxes.shape) == 3:
offset = offset.unsqueeze(1)
return boxes + offset
def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
x0, y0, _, _ = crop_box
offset = torch.tensor([[x0, y0]], device=points.device)
# Check if points has a channel dimension
if len(points.shape) == 3:
offset = offset.unsqueeze(1)
return points + offset
def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int,
orig_w: int) -> torch.Tensor:
x0, y0, x1, y1 = crop_box
if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
return masks
# Coordinate transform masks
pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
pad = (x0, pad_x - x0, y0, pad_y - y0)
return torch.nn.functional.pad(masks, pad, value=0)
def remove_small_regions(mask: np.ndarray, area_thresh: float,
mode: str) -> Tuple[np.ndarray, bool]:
"""Removes small disconnected regions and holes in a mask.
Returns the mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ['holes', 'islands']
correct_holes = mode == 'holes'
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(
working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
from pycocotools import mask as mask_utils # type: ignore
h, w = uncompressed_rle['size']
rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
rle['counts'] = rle['counts'].decode(
'utf-8') # Necessary to serialize with json
return rle
def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
"""Calculates boxes in XYXY format around masks.
Return [0,0,0,0] for an empty mask. For input shape C1xC2x...xHxW, the
output shape is C1xC2x...x4.
"""
# torch.max below raises an error on empty inputs, just skip in this case
if torch.numel(masks) == 0:
return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
# Normalize shape to CxHxW
shape = masks.shape
h, w = shape[-2:]
if len(shape) > 2:
masks = masks.flatten(0, -3)
else:
masks = masks.unsqueeze(0)
# Get top and bottom edges
in_height, _ = torch.max(masks, dim=-1)
in_height_coords = in_height * torch.arange(
h, device=in_height.device)[None, :]
bottom_edges, _ = torch.max(in_height_coords, dim=-1)
in_height_coords = in_height_coords + h * (~in_height)
top_edges, _ = torch.min(in_height_coords, dim=-1)
# Get left and right edges
in_width, _ = torch.max(masks, dim=-2)
in_width_coords = in_width * torch.arange(
w, device=in_width.device)[None, :]
right_edges, _ = torch.max(in_width_coords, dim=-1)
in_width_coords = in_width_coords + w * (~in_width)
left_edges, _ = torch.min(in_width_coords, dim=-1)
# If the mask is empty the right edge will be to the left of the left edge.
# Replace these boxes with [0, 0, 0, 0]
empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
out = torch.stack([left_edges, top_edges, right_edges, bottom_edges],
dim=-1)
out = out * (~empty_filter).unsqueeze(-1)
# Return to original shape
if len(shape) > 2:
out = out.reshape(*shape[:-2], 4)
else:
out = out[0]
return out

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from copy import deepcopy
from typing import Tuple
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize # type: ignore
from torchvision.transforms.functional import to_pil_image
from mmseg.registry import TRANSFORMS
@TRANSFORMS.register_module()
class ResizeLongestSide:
"""Resizes images to longest side 'target_length', as well as provides
methods for resizing coordinates and boxes.
Provides methods for transforming both numpy array and batched torch
tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_length = target_length
def apply_image(self, image: np.ndarray) -> np.ndarray:
"""Expects a numpy array with shape HxWxC in uint8 format."""
target_size = self.get_preprocess_shape(image.shape[0], image.shape[1],
self.target_length)
return np.array(resize(to_pil_image(image), target_size))
def apply_coords(self, coords: np.ndarray,
original_size: Tuple[int, ...]) -> np.ndarray:
"""Expects a numpy array of length 2 in the final dimension.
Requires the original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(original_size[0],
original_size[1],
self.target_length)
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes(self, boxes: np.ndarray,
original_size: Tuple[int, ...]) -> np.ndarray:
"""Expects a numpy array shape Bx4.
Requires the original image size in (H, W) format.
"""
boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
"""Expects batched images with shape BxCxHxW and float format.
This transformation may not exactly match apply_image. apply_image is
the transformation expected by the model.
"""
# Expects an image in BCHW format. May not exactly match apply_image.
target_size = self.get_preprocess_shape(image.shape[0], image.shape[1],
self.target_length)
return F.interpolate(
image,
target_size,
mode='bilinear',
align_corners=False,
antialias=True)
def apply_coords_torch(self, coords: torch.Tensor,
original_size: Tuple[int, ...]) -> torch.Tensor:
"""Expects a torch tensor with length 2 in the last dimension.
Requires the original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(original_size[0],
original_size[1],
self.target_length)
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes_torch(self, boxes: torch.Tensor,
original_size: Tuple[int, ...]) -> torch.Tensor:
"""Expects a torch tensor with shape Bx4.
Requires the original image size in (H, W) format.
"""
boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
@staticmethod
def get_preprocess_shape(oldh: int, oldw: int,
long_side_length: int) -> Tuple[int, int]:
"""Compute the output size given input size and target long side
length."""
scale = long_side_length * 1.0 / max(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)

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